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Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (23.3.2)
Collecting pip
Using cached pip-24.0-py3-none-any.whl.metadata (3.6 kB)
Using cached pip-24.0-py3-none-any.whl (2.1 MB)
Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 23.3.2
Uninstalling pip-23.3.2:
Successfully uninstalled pip-23.3.2
Successfully installed pip-24.0
Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (69.5.1)
Requirement already satisfied: wheel in /opt/conda/lib/python3.10/site-packages (0.43.0)
Collecting mxnet<2.0.0
Using cached mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl.metadata (3.4 kB)
Collecting bokeh==2.0.1
Using cached bokeh-2.0.1-py3-none-any.whl
Requirement already satisfied: PyYAML>=3.10 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (6.0.1)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (2.9.0)
Requirement already satisfied: Jinja2>=2.7 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (3.1.3)
Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (1.26.4)
Requirement already satisfied: pillow>=4.0 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (9.5.0)
Requirement already satisfied: packaging>=16.8 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (23.2)
Requirement already satisfied: tornado>=5 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (6.4)
Requirement already satisfied: typing-extensions>=3.7.4 in /opt/conda/lib/python3.10/site-packages (from bokeh==2.0.1) (4.5.0)
Requirement already satisfied: requests<3,>=2.20.0 in /opt/conda/lib/python3.10/site-packages (from mxnet<2.0.0) (2.31.0)
Collecting graphviz<0.9.0,>=0.8.1 (from mxnet<2.0.0)
Using cached graphviz-0.8.4-py2.py3-none-any.whl.metadata (6.4 kB)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from Jinja2>=2.7->bokeh==2.0.1) (2.1.5)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.1->bokeh==2.0.1) (1.16.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (3.6)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (1.26.18)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.20.0->mxnet<2.0.0) (2024.2.2)
Using cached mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)
Using cached graphviz-0.8.4-py2.py3-none-any.whl (16 kB)
Installing collected packages: graphviz, mxnet, bokeh
Attempting uninstall: graphviz
Found existing installation: graphviz 0.20.3
Uninstalling graphviz-0.20.3:
Successfully uninstalled graphviz-0.20.3
Successfully installed bokeh-2.0.1 graphviz-0.8.4 mxnet-1.9.1
Requirement already satisfied: autogluon in /opt/conda/lib/python3.10/site-packages (0.8.2)
Requirement already satisfied: autogluon.core==0.8.2 in /opt/conda/lib/python3.10/site-packages (from autogluon.core[all]==0.8.2->autogluon) (0.8.2)
Requirement already satisfied: autogluon.features==0.8.2 in /opt/conda/lib/python3.10/site-packages (from autogluon) (0.8.2)
Requirement already satisfied: autogluon.tabular==0.8.2 in /opt/conda/lib/python3.10/site-packages (from autogluon.tabular[all]==0.8.2->autogluon) (0.8.2)
Requirement already satisfied: autogluon.multimodal==0.8.2 in /opt/conda/lib/python3.10/site-packages (from autogluon) (0.8.2)
Requirement already satisfied: autogluon.timeseries==0.8.2 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries[all]==0.8.2->autogluon) (0.8.2)
Requirement already satisfied: numpy<1.27,>=1.21 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.26.4)
Requirement already satisfied: scipy<1.12,>=1.5.4 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.11.4)
Requirement already satisfied: scikit-learn<1.5,>=1.3.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.4.2)
Requirement already satisfied: networkx<4,>=3.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.3)
Requirement already satisfied: pandas<2.2.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (2.1.4)
Requirement already satisfied: tqdm<5,>=4.38 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (4.66.2)
Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (2.31.0)
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (3.8.4)
Requirement already satisfied: boto3<2,>=1.10 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.34.51)
Requirement already satisfied: autogluon.common==0.8.2 in /opt/conda/lib/python3.10/site-packages (from autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (0.8.2)
Collecting hyperopt<0.2.8,>=0.2.7 (from autogluon.core[all]==0.8.2->autogluon)
Downloading hyperopt-0.2.7-py2.py3-none-any.whl.metadata (1.7 kB)
Requirement already satisfied: pydantic<2.0,>=1.10.4 in /opt/conda/lib/python3.10/site-packages (from autogluon.core[all]==0.8.2->autogluon) (1.10.14)
Collecting ray<2.7,>=2.6.3 (from ray[tune]<2.7,>=2.6.3; extra == "all"->autogluon.core[all]==0.8.2->autogluon)
Downloading ray-2.6.3-cp310-cp310-manylinux2014_x86_64.whl.metadata (12 kB)
Requirement already satisfied: Pillow<9.6,>=9.3 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (9.5.0)
Requirement already satisfied: torch<2.1,>=1.13 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (2.0.0.post101)
Requirement already satisfied: pytorch-lightning<2.1,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (2.0.9)
Requirement already satisfied: jsonschema<4.18,>=4.14 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (4.17.3)
Requirement already satisfied: seqeval<1.3.0,>=1.2.2 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (1.2.2)
Requirement already satisfied: evaluate<0.5.0,>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.4.1)
Requirement already satisfied: accelerate<0.22.0,>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.21.0)
Requirement already satisfied: transformers<4.32.0,>=4.31.0 in /opt/conda/lib/python3.10/site-packages (from transformers[sentencepiece]<4.32.0,>=4.31.0->autogluon.multimodal==0.8.2->autogluon) (4.31.0)
Requirement already satisfied: timm<0.10.0,>=0.9.5 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.9.16)
Requirement already satisfied: torchvision<0.16.0,>=0.14.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.15.2a0+072ec57)
Requirement already satisfied: scikit-image<0.20.0,>=0.19.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.19.3)
Requirement already satisfied: text-unidecode<1.4,>=1.3 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (1.3)
Requirement already satisfied: torchmetrics<1.1.0,>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (1.0.3)
Requirement already satisfied: nptyping<2.5.0,>=1.4.4 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (2.4.1)
Requirement already satisfied: omegaconf<2.3.0,>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (2.2.3)
Requirement already satisfied: pytorch-metric-learning<2.0,>=1.3.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (1.7.3)
Requirement already satisfied: nlpaug<1.2.0,>=1.1.10 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (1.1.11)
Requirement already satisfied: nltk<4.0.0,>=3.4.5 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (3.8.1)
Requirement already satisfied: openmim<0.4.0,>=0.3.7 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.3.7)
Requirement already satisfied: defusedxml<0.7.2,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.7.1)
Requirement already satisfied: jinja2<3.2,>=3.0.3 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (3.1.3)
Requirement already satisfied: tensorboard<3,>=2.9 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (2.12.3)
Requirement already satisfied: pytesseract<0.3.11,>=0.3.9 in /opt/conda/lib/python3.10/site-packages (from autogluon.multimodal==0.8.2->autogluon) (0.3.10)
Requirement already satisfied: catboost<1.3,>=1.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.tabular[all]==0.8.2->autogluon) (1.2.3)
Requirement already satisfied: xgboost<1.8,>=1.6 in /opt/conda/lib/python3.10/site-packages (from autogluon.tabular[all]==0.8.2->autogluon) (1.7.6)
Requirement already satisfied: fastai<2.8,>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.tabular[all]==0.8.2->autogluon) (2.7.14)
Requirement already satisfied: lightgbm<3.4,>=3.3 in /opt/conda/lib/python3.10/site-packages (from autogluon.tabular[all]==0.8.2->autogluon) (3.3.5)
Requirement already satisfied: joblib<2,>=1.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (1.4.0)
Requirement already satisfied: statsmodels<0.15,>=0.13.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.14.1)
Requirement already satisfied: gluonts<0.14,>=0.13.1 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.13.7)
Requirement already satisfied: statsforecast<1.5,>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (1.4.0)
Requirement already satisfied: mlforecast<0.7.4,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.7.3)
Requirement already satisfied: ujson<6,>=5 in /opt/conda/lib/python3.10/site-packages (from autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (5.9.0)
Requirement already satisfied: psutil<6,>=5.7.3 in /opt/conda/lib/python3.10/site-packages (from autogluon.common==0.8.2->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (5.9.8)
Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from autogluon.common==0.8.2->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (69.5.1)
Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from accelerate<0.22.0,>=0.21.0->autogluon.multimodal==0.8.2->autogluon) (23.2)
Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate<0.22.0,>=0.21.0->autogluon.multimodal==0.8.2->autogluon) (6.0.1)
Requirement already satisfied: botocore<1.35.0,>=1.34.51 in /opt/conda/lib/python3.10/site-packages (from boto3<2,>=1.10->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.34.51)
Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from boto3<2,>=1.10->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (1.0.1)
Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /opt/conda/lib/python3.10/site-packages (from boto3<2,>=1.10->autogluon.core==0.8.2->autogluon.core[all]==0.8.2->autogluon) (0.10.1)
Requirement already satisfied: graphviz in /opt/conda/lib/python3.10/site-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (0.8.4)
Requirement already satisfied: plotly in /opt/conda/lib/python3.10/site-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (5.19.0)
Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from catboost<1.3,>=1.1->autogluon.tabular[all]==0.8.2->autogluon) (1.16.0)
Requirement already satisfied: datasets>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (2.18.0)
Requirement already satisfied: dill in /opt/conda/lib/python3.10/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (0.3.8)
Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (3.4.1)
Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (0.70.16)
Requirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]>=2021.05.0->evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (2023.6.0)
Requirement already satisfied: huggingface-hub>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (0.22.2)
Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.10/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==0.8.2->autogluon) (0.18.0)
Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (24.0)
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Requirement already satisfied: fastcore<1.6,>=1.5.29 in /opt/conda/lib/python3.10/site-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.5.29)
Requirement already satisfied: fastprogress>=0.2.4 in /opt/conda/lib/python3.10/site-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (1.0.3)
Requirement already satisfied: spacy<4 in /opt/conda/lib/python3.10/site-packages (from fastai<2.8,>=2.3.1->autogluon.tabular[all]==0.8.2->autogluon) (3.7.3)
Requirement already satisfied: toolz~=0.10 in /opt/conda/lib/python3.10/site-packages (from gluonts<0.14,>=0.13.1->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.12.1)
Requirement already satisfied: typing-extensions~=4.0 in /opt/conda/lib/python3.10/site-packages (from gluonts<0.14,>=0.13.1->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (4.5.0)
Requirement already satisfied: future in /opt/conda/lib/python3.10/site-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon) (1.0.0)
Requirement already satisfied: cloudpickle in /opt/conda/lib/python3.10/site-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon) (2.2.1)
Collecting py4j (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==0.8.2->autogluon)
Downloading py4j-0.10.9.7-py2.py3-none-any.whl.metadata (1.5 kB)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2<3.2,>=3.0.3->autogluon.multimodal==0.8.2->autogluon) (2.1.5)
Requirement already satisfied: attrs>=17.4.0 in /opt/conda/lib/python3.10/site-packages (from jsonschema<4.18,>=4.14->autogluon.multimodal==0.8.2->autogluon) (23.2.0)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.10/site-packages (from jsonschema<4.18,>=4.14->autogluon.multimodal==0.8.2->autogluon) (0.20.0)
Requirement already satisfied: wheel in /opt/conda/lib/python3.10/site-packages (from lightgbm<3.4,>=3.3->autogluon.tabular[all]==0.8.2->autogluon) (0.43.0)
Requirement already satisfied: numba in /opt/conda/lib/python3.10/site-packages (from mlforecast<0.7.4,>=0.7.0->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.59.1)
Requirement already satisfied: window-ops in /opt/conda/lib/python3.10/site-packages (from mlforecast<0.7.4,>=0.7.0->autogluon.timeseries==0.8.2->autogluon.timeseries[all]==0.8.2->autogluon) (0.0.15)
Requirement already satisfied: gdown>=4.0.0 in /opt/conda/lib/python3.10/site-packages (from nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==0.8.2->autogluon) (5.1.0)
Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk<4.0.0,>=3.4.5->autogluon.multimodal==0.8.2->autogluon) (8.1.7)
Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk<4.0.0,>=3.4.5->autogluon.multimodal==0.8.2->autogluon) (2023.12.25)
Requirement already satisfied: antlr4-python3-runtime==4.9.* in /opt/conda/lib/python3.10/site-packages (from omegaconf<2.3.0,>=2.1.1->autogluon.multimodal==0.8.2->autogluon) (4.9.3)
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Building wheels for collected packages: gpustat
Building wheel for gpustat (pyproject.toml) ... done
Created wheel for gpustat: filename=gpustat-1.1.1-py3-none-any.whl size=26532 sha256=1f24db2b5b2a195daed7549b1dafb1a14eaf220729e499a5e3d6250087ff3578
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Successfully built gpustat
Installing collected packages: py4j, py-spy, opencensus-context, nvidia-ml-py, distlib, colorful, tensorboardX, proto-plus, platformdirs, googleapis-common-protos, blessed, virtualenv, ray, hyperopt, gpustat, google-api-core, aiohttp-cors, opencensus
Attempting uninstall: platformdirs
Found existing installation: platformdirs 4.2.0
Uninstalling platformdirs-4.2.0:
Successfully uninstalled platformdirs-4.2.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
sparkmagic 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.4 which is incompatible.
Successfully installed aiohttp-cors-0.7.0 blessed-1.20.0 colorful-0.5.6 distlib-0.3.8 google-api-core-2.18.0 googleapis-common-protos-1.63.0 gpustat-1.1.1 hyperopt-0.2.7 nvidia-ml-py-12.550.52 opencensus-0.11.4 opencensus-context-0.1.3 platformdirs-3.11.0 proto-plus-1.23.0 py-spy-0.3.14 py4j-0.10.9.7 ray-2.6.3 tensorboardX-2.6.2.2 virtualenv-20.21.0
In [18]:
!echo '{"username":"USERNAME","key":"KEY"}' > ~/.kaggle/kaggle.json
In [19]:
!mkdir -p .kaggle
!touch .kaggle/kaggle.json
!chmod 600 .kaggle/kaggle.json
In [20]:
import json
kaggle_username = "divyachauhan3301"
kaggle_key = "eb781f878f360e585e1aeb9e7828a87d"
# Save API token the kaggle.json file
with open(".kaggle/kaggle.json", "w") as f:
f.write(json.dumps({"username": kaggle_username, "key": kaggle_key}))
In [21]:
import pandas as pd
from autogluon.tabular import TabularPredictor
In [ ]:
In [22]:
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
submission = pd.read_csv('sampleSubmission.csv')
In [23]:
train.head()
Out[23]:
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 |
In [24]:
test.head()
Out[24]:
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-20 00:00:00 | 1 | 0 | 1 | 1 | 10.66 | 11.365 | 56 | 26.0027 |
| 1 | 2011-01-20 01:00:00 | 1 | 0 | 1 | 1 | 10.66 | 13.635 | 56 | 0.0000 |
| 2 | 2011-01-20 02:00:00 | 1 | 0 | 1 | 1 | 10.66 | 13.635 | 56 | 0.0000 |
| 3 | 2011-01-20 03:00:00 | 1 | 0 | 1 | 1 | 10.66 | 12.880 | 56 | 11.0014 |
| 4 | 2011-01-20 04:00:00 | 1 | 0 | 1 | 1 | 10.66 | 12.880 | 56 | 11.0014 |
In [25]:
submission.head()
Out[25]:
| datetime | count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 0 |
| 1 | 2011-01-20 01:00:00 | 0 |
| 2 | 2011-01-20 02:00:00 | 0 |
| 3 | 2011-01-20 03:00:00 | 0 |
| 4 | 2011-01-20 04:00:00 | 0 |
In [ ]:
predictor = TabularPredictor(
label="count", problem_type="regression", eval_metric="rmse"
).fit(
train_data=train.drop(['casual', 'registered'], axis=1),
time_limit=600,
presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels/ag-20240430_032935"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20240430_032935"
AutoGluon Version: 0.8.2
Python Version: 3.10.14
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Sat Mar 23 09:49:55 UTC 2024
Disk Space Avail: 5.18 GB / 5.36 GB (96.7%)
WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception.
We recommend a minimum available disk space of 10 GB, and large datasets may require more.
Train Data Rows: 10886
Train Data Columns: 9
Label Column: count
Preprocessing data ...
/opt/conda/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context("mode.use_inf_as_na", True): # treat None, NaN, INF, NINF as NA
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 2195.09 MB
Train Data (Original) Memory Usage: 1.52 MB (0.1% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
/opt/conda/lib/python3.10/site-packages/autogluon/features/generators/fillna.py:58: FutureWarning: The 'downcast' keyword in fillna is deprecated and will be removed in a future version. Use res.infer_objects(copy=False) to infer non-object dtype, or pd.to_numeric with the 'downcast' keyword to downcast numeric results.
X.fillna(self._fillna_feature_map, inplace=True, downcast=False)
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
('object', ['datetime_as_object']) : 1 | ['datetime']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['season', 'weather', 'humidity']
('int', ['bool']) : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.1s = Fit runtime
9 features in original data used to generate 13 features in processed data.
Train Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.18s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
User-specified model hyperparameters to be fit:
{
'NN_TORCH': {},
'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
'CAT': {},
'XGB': {},
'FASTAI': {},
'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.78s of the 599.81s of remaining time.
-101.5462 = Validation score (-root_mean_squared_error)
0.04s = Training runtime
0.05s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 396.75s of the 596.78s of remaining time.
-84.1251 = Validation score (-root_mean_squared_error)
0.03s = Training runtime
0.07s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 396.61s of the 596.65s of remaining time.
Will use sequential fold fitting strategy because import of ray failed. Reason: ray is required to train folds in parallel for TabularPredictor or HPO for MultiModalPredictor. A quick tip is to install via `pip install ray==2.6.3`
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
/opt/conda/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
warnings.warn(
/opt/conda/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning:
Dask dataframe query planning is disabled because dask-expr is not installed.
You can install it with `pip install dask[dataframe]` or `conda install dask`.
This will raise in a future version.
warnings.warn(msg, FutureWarning)
[1000] valid_set's rmse: 131.684 [2000] valid_set's rmse: 130.67 [3000] valid_set's rmse: 130.626 [1000] valid_set's rmse: 135.592 [1000] valid_set's rmse: 133.481 [2000] valid_set's rmse: 132.323 [3000] valid_set's rmse: 131.618 [4000] valid_set's rmse: 131.443 [5000] valid_set's rmse: 131.265 [6000] valid_set's rmse: 131.277 [7000] valid_set's rmse: 131.443 [1000] valid_set's rmse: 128.503 [2000] valid_set's rmse: 127.654 [3000] valid_set's rmse: 127.227 [4000] valid_set's rmse: 127.105 [1000] valid_set's rmse: 134.135 [2000] valid_set's rmse: 132.272 [3000] valid_set's rmse: 131.286 [4000] valid_set's rmse: 130.752 [5000] valid_set's rmse: 130.363 [6000] valid_set's rmse: 130.509 [1000] valid_set's rmse: 136.168 [2000] valid_set's rmse: 135.138 [3000] valid_set's rmse: 135.029 [1000] valid_set's rmse: 134.061 [2000] valid_set's rmse: 133.034 [3000] valid_set's rmse: 132.182 [4000] valid_set's rmse: 131.997 [5000] valid_set's rmse: 131.643 [6000] valid_set's rmse: 131.504 [7000] valid_set's rmse: 131.574 [1000] valid_set's rmse: 132.912 [2000] valid_set's rmse: 131.703 [3000] valid_set's rmse: 131.117 [4000] valid_set's rmse: 130.82 [5000] valid_set's rmse: 130.673 [6000] valid_set's rmse: 130.708
-131.4609 = Validation score (-root_mean_squared_error) 48.55s = Training runtime 6.38s = Validation runtime Fitting model: LightGBM_BAG_L1 ... Training model for up to 335.3s of the 535.33s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000] valid_set's rmse: 130.818
In [29]:
predictor.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -52.743784 11.395770 525.655306 0.000636 0.279644 3 True 16
1 RandomForestMSE_BAG_L2 -53.391974 10.371316 417.029261 0.773592 39.790365 2 True 13
2 ExtraTreesMSE_BAG_L2 -53.746435 10.315211 388.384858 0.717486 11.145962 2 True 15
3 LightGBM_BAG_L2 -55.043589 9.857359 389.048964 0.259634 11.810067 2 True 12
4 CatBoost_BAG_L2 -55.358038 9.644422 462.629268 0.046697 85.390371 2 True 14
5 LightGBMXT_BAG_L2 -59.788370 12.972428 422.900408 3.374703 45.661511 2 True 11
6 KNeighborsDist_BAG_L1 -84.125061 0.068755 0.030827 0.068755 0.030827 1 True 2
7 WeightedEnsemble_L2 -84.125061 0.069344 0.411616 0.000590 0.380789 2 True 10
8 KNeighborsUnif_BAG_L1 -101.546199 0.045192 0.035783 0.045192 0.035783 1 True 1
9 RandomForestMSE_BAG_L1 -116.548359 0.629014 14.247486 0.629014 14.247486 1 True 5
10 ExtraTreesMSE_BAG_L1 -124.600676 0.612904 7.997195 0.612904 7.997195 1 True 7
11 CatBoost_BAG_L1 -130.498580 0.073415 232.885981 0.073415 232.885981 1 True 6
12 LightGBM_BAG_L1 -131.054162 1.411598 12.648717 1.411598 12.648717 1 True 4
13 LightGBMXT_BAG_L1 -131.460909 6.384120 48.548435 6.384120 48.548435 1 True 3
14 XGBoost_BAG_L1 -132.487303 0.134549 3.372401 0.134549 3.372401 1 True 9
15 NeuralNetFastAI_BAG_L1 -137.973239 0.238179 57.472072 0.238179 57.472072 1 True 8
Number of models trained: 16
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_XGBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['season', 'weather', 'humidity']
('int', ['bool']) : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
Out[29]:
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
'KNeighborsDist_BAG_L1': -84.12506123181602,
'LightGBMXT_BAG_L1': -131.46090891834504,
'LightGBM_BAG_L1': -131.054161598899,
'RandomForestMSE_BAG_L1': -116.54835939455667,
'CatBoost_BAG_L1': -130.49858036848312,
'ExtraTreesMSE_BAG_L1': -124.60067564699747,
'NeuralNetFastAI_BAG_L1': -137.97323934290566,
'XGBoost_BAG_L1': -132.4873031302656,
'WeightedEnsemble_L2': -84.12506123181602,
'LightGBMXT_BAG_L2': -59.78836974728784,
'LightGBM_BAG_L2': -55.04358945978129,
'RandomForestMSE_BAG_L2': -53.391974165298606,
'CatBoost_BAG_L2': -55.358038111004205,
'ExtraTreesMSE_BAG_L2': -53.74643451738374,
'WeightedEnsemble_L3': -52.74378370945104},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],
'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],
'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],
'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],
'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],
'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],
'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],
'NeuralNetFastAI_BAG_L1': ['NeuralNetFastAI_BAG_L1'],
'XGBoost_BAG_L1': ['XGBoost_BAG_L1'],
'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],
'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],
'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],
'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],
'CatBoost_BAG_L2': ['CatBoost_BAG_L2'],
'ExtraTreesMSE_BAG_L2': ['ExtraTreesMSE_BAG_L2'],
'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},
'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.035782814025878906,
'KNeighborsDist_BAG_L1': 0.030827045440673828,
'LightGBMXT_BAG_L1': 48.54843473434448,
'LightGBM_BAG_L1': 12.648716926574707,
'RandomForestMSE_BAG_L1': 14.247486352920532,
'CatBoost_BAG_L1': 232.88598132133484,
'ExtraTreesMSE_BAG_L1': 7.997194766998291,
'NeuralNetFastAI_BAG_L1': 57.4720721244812,
'XGBoost_BAG_L1': 3.3724005222320557,
'WeightedEnsemble_L2': 0.38078927993774414,
'LightGBMXT_BAG_L2': 45.66151142120361,
'LightGBM_BAG_L2': 11.810067176818848,
'RandomForestMSE_BAG_L2': 39.790364503860474,
'CatBoost_BAG_L2': 85.39037132263184,
'ExtraTreesMSE_BAG_L2': 11.14596152305603,
'WeightedEnsemble_L3': 0.2796444892883301},
'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.04519152641296387,
'KNeighborsDist_BAG_L1': 0.06875467300415039,
'LightGBMXT_BAG_L1': 6.384119749069214,
'LightGBM_BAG_L1': 1.4115982055664062,
'RandomForestMSE_BAG_L1': 0.629014253616333,
'CatBoost_BAG_L1': 0.07341456413269043,
'ExtraTreesMSE_BAG_L1': 0.6129038333892822,
'NeuralNetFastAI_BAG_L1': 0.23817920684814453,
'XGBoost_BAG_L1': 0.13454890251159668,
'WeightedEnsemble_L2': 0.0005896091461181641,
'LightGBMXT_BAG_L2': 3.3747026920318604,
'LightGBM_BAG_L2': 0.25963425636291504,
'RandomForestMSE_BAG_L2': 0.7735915184020996,
'CatBoost_BAG_L2': 0.04669690132141113,
'ExtraTreesMSE_BAG_L2': 0.7174859046936035,
'WeightedEnsemble_L3': 0.0006363391876220703},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'KNeighborsDist_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'LightGBMXT_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'XGBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMXT_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -52.743784 11.395770 525.655306
1 RandomForestMSE_BAG_L2 -53.391974 10.371316 417.029261
2 ExtraTreesMSE_BAG_L2 -53.746435 10.315211 388.384858
3 LightGBM_BAG_L2 -55.043589 9.857359 389.048964
4 CatBoost_BAG_L2 -55.358038 9.644422 462.629268
5 LightGBMXT_BAG_L2 -59.788370 12.972428 422.900408
6 KNeighborsDist_BAG_L1 -84.125061 0.068755 0.030827
7 WeightedEnsemble_L2 -84.125061 0.069344 0.411616
8 KNeighborsUnif_BAG_L1 -101.546199 0.045192 0.035783
9 RandomForestMSE_BAG_L1 -116.548359 0.629014 14.247486
10 ExtraTreesMSE_BAG_L1 -124.600676 0.612904 7.997195
11 CatBoost_BAG_L1 -130.498580 0.073415 232.885981
12 LightGBM_BAG_L1 -131.054162 1.411598 12.648717
13 LightGBMXT_BAG_L1 -131.460909 6.384120 48.548435
14 XGBoost_BAG_L1 -132.487303 0.134549 3.372401
15 NeuralNetFastAI_BAG_L1 -137.973239 0.238179 57.472072
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000636 0.279644 3 True
1 0.773592 39.790365 2 True
2 0.717486 11.145962 2 True
3 0.259634 11.810067 2 True
4 0.046697 85.390371 2 True
5 3.374703 45.661511 2 True
6 0.068755 0.030827 1 True
7 0.000590 0.380789 2 True
8 0.045192 0.035783 1 True
9 0.629014 14.247486 1 True
10 0.612904 7.997195 1 True
11 0.073415 232.885981 1 True
12 1.411598 12.648717 1 True
13 6.384120 48.548435 1 True
14 0.134549 3.372401 1 True
15 0.238179 57.472072 1 True
fit_order
0 16
1 13
2 15
3 12
4 14
5 11
6 2
7 10
8 1
9 5
10 7
11 6
12 4
13 3
14 9
15 8 }
In [30]:
predictions = predictor.predict(test)
predictions = {'datetime': test['datetime'], 'Pred_count': predictions}
predictions = pd.DataFrame(data=predictions)
predictions.head()
/opt/conda/lib/python3.10/site-packages/autogluon/features/generators/fillna.py:58: FutureWarning: The 'downcast' keyword in fillna is deprecated and will be removed in a future version. Use res.infer_objects(copy=False) to infer non-object dtype, or pd.to_numeric with the 'downcast' keyword to downcast numeric results. X.fillna(self._fillna_feature_map, inplace=True, downcast=False)
Out[30]:
| datetime | Pred_count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 23.875008 |
| 1 | 2011-01-20 01:00:00 | 41.514889 |
| 2 | 2011-01-20 02:00:00 | 46.342697 |
| 3 | 2011-01-20 03:00:00 | 49.907799 |
| 4 | 2011-01-20 04:00:00 | 52.934887 |
In [ ]:
In [31]:
predictions.describe()
Out[31]:
| Pred_count | |
|---|---|
| count | 6493.000000 |
| mean | 101.133789 |
| std | 90.489120 |
| min | 2.932264 |
| 25% | 21.513674 |
| 50% | 62.904556 |
| 75% | 171.659409 |
| max | 365.803619 |
In [32]:
negative = predictions.groupby(predictions['Pred_count'])
# lambda function
def minus(val):
return val[val < 0].sum()
print(negative['Pred_count'].agg([('negcount', minus)]))
negcount Pred_count 2.932264 0.0 2.978237 0.0 3.043153 0.0 3.071162 0.0 3.097124 0.0 ... ... 363.410614 0.0 365.069702 0.0 365.383972 0.0 365.449768 0.0 365.803619 0.0 [6248 rows x 1 columns]
In [33]:
predictions[predictions['Pred_count']<0] = 0
In [34]:
predictions.describe()
Out[34]:
| Pred_count | |
|---|---|
| count | 6493.000000 |
| mean | 101.133789 |
| std | 90.489120 |
| min | 2.932264 |
| 25% | 21.513674 |
| 50% | 62.904556 |
| 75% | 171.659409 |
| max | 365.803619 |
In [35]:
predictions.head()
Out[35]:
| datetime | Pred_count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 23.875008 |
| 1 | 2011-01-20 01:00:00 | 41.514889 |
| 2 | 2011-01-20 02:00:00 | 46.342697 |
| 3 | 2011-01-20 03:00:00 | 49.907799 |
| 4 | 2011-01-20 04:00:00 | 52.934887 |
In [36]:
submission["count"] = predictions['Pred_count']
submission.to_csv("submission.csv", index=False)
In [43]:
import kaggle
In [44]:
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "first raw submission"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 665kB/s] Successfully submitted to Bike Sharing Demand
In [45]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- --------------------------------- -------- ----------- ------------ submission.csv 2024-04-30 03:42:29 first raw submission pending submission_new_hpo.csv 2024-04-30 02:53:22 new features with hyperparameters complete 0.48188 0.48188 submission_new_features.csv 2024-04-30 02:37:04 new features complete 0.6741 0.6741 submission.csv 2024-04-30 02:20:52 first raw submission complete 1.80512 1.80512
In [46]:
train.hist()
Out[46]:
array([[<Axes: title={'center': 'datetime'}>,
<Axes: title={'center': 'season'}>,
<Axes: title={'center': 'holiday'}>,
<Axes: title={'center': 'workingday'}>],
[<Axes: title={'center': 'weather'}>,
<Axes: title={'center': 'temp'}>,
<Axes: title={'center': 'atemp'}>,
<Axes: title={'center': 'humidity'}>],
[<Axes: title={'center': 'windspeed'}>,
<Axes: title={'center': 'casual'}>,
<Axes: title={'center': 'registered'}>,
<Axes: title={'center': 'count'}>],
[<Axes: title={'center': 'year'}>,
<Axes: title={'center': 'month'}>,
<Axes: title={'center': 'day'}>,
<Axes: title={'center': 'hour'}>]], dtype=object)
In [47]:
train['datetime'] = pd.to_datetime(train['datetime'])
test['datetime'] = pd.to_datetime(test['datetime'])
# Access year, month, and day
train['year'] = train['datetime'].dt.year
train['month'] = train['datetime'].dt.month
train['day'] = train['datetime'].dt.day
train['hour'] = train['datetime'].dt.hour
test['year'] = test['datetime'].dt.year
test['month'] = test['datetime'].dt.month
test['day'] = test['datetime'].dt.day
test['hour'] = test['datetime'].dt.hour
In [48]:
train["season"] = train["season"].astype("category")
train["weather"] = train["weather"].astype("category")
test["season"] = test["season"].astype("category")
test["weather"] = test["weather"].astype("category")
In [49]:
train.head()
Out[49]:
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | year | month | day | hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 | 2011 | 1 | 1 | 0 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 | 2011 | 1 | 1 | 1 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 | 2011 | 1 | 1 | 2 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 | 2011 | 1 | 1 | 3 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 | 2011 | 1 | 1 | 4 |
In [50]:
train.hist()
Out[50]:
array([[<Axes: title={'center': 'datetime'}>,
<Axes: title={'center': 'holiday'}>,
<Axes: title={'center': 'workingday'}>,
<Axes: title={'center': 'temp'}>],
[<Axes: title={'center': 'atemp'}>,
<Axes: title={'center': 'humidity'}>,
<Axes: title={'center': 'windspeed'}>,
<Axes: title={'center': 'casual'}>],
[<Axes: title={'center': 'registered'}>,
<Axes: title={'center': 'count'}>,
<Axes: title={'center': 'year'}>,
<Axes: title={'center': 'month'}>],
[<Axes: title={'center': 'day'}>,
<Axes: title={'center': 'hour'}>, <Axes: >, <Axes: >]],
dtype=object)
In [51]:
predictor_new_features = TabularPredictor(
label="count", problem_type="regression", eval_metric="rmse"
).fit(
train_data=train.drop(['casual', 'registered'], axis=1),
time_limit=600,
presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels/ag-20240430_034236"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20240430_034236"
AutoGluon Version: 0.8.2
Python Version: 3.10.14
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Sat Mar 23 09:49:55 UTC 2024
Disk Space Avail: 3.81 GB / 5.36 GB (71.1%)
WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception.
We recommend a minimum available disk space of 10 GB, and large datasets may require more.
Train Data Rows: 10886
Train Data Columns: 13
Label Column: count
Preprocessing data ...
/opt/conda/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context("mode.use_inf_as_na", True): # treat None, NaN, INF, NINF as NA
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 2131.48 MB
Train Data (Original) Memory Usage: 0.81 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
0.9s = Fit runtime
13 features in original data used to generate 15 features in processed data.
Train Data (Processed) Memory Usage: 0.8 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.98s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
User-specified model hyperparameters to be fit:
{
'NN_TORCH': {},
'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
'CAT': {},
'XGB': {},
'FASTAI': {},
'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.24s of the 599.01s of remaining time.
-101.5462 = Validation score (-root_mean_squared_error)
0.06s = Training runtime
0.06s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.06s of the 598.83s of remaining time.
-84.1251 = Validation score (-root_mean_squared_error)
0.03s = Training runtime
0.05s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 398.94s of the 598.71s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000] valid_set's rmse: 35.722 [2000] valid_set's rmse: 34.0646 [3000] valid_set's rmse: 33.7501 [4000] valid_set's rmse: 33.5663 [5000] valid_set's rmse: 33.5927 [1000] valid_set's rmse: 36.6943 [2000] valid_set's rmse: 34.7009 [3000] valid_set's rmse: 34.2654 [4000] valid_set's rmse: 34.0805 [5000] valid_set's rmse: 34.0068 [6000] valid_set's rmse: 33.9926 [7000] valid_set's rmse: 34.0148 [8000] valid_set's rmse: 34.0505 [1000] valid_set's rmse: 37.0225 [2000] valid_set's rmse: 34.5264 [3000] valid_set's rmse: 33.9428 [4000] valid_set's rmse: 33.6752 [5000] valid_set's rmse: 33.5411 [6000] valid_set's rmse: 33.4628 [7000] valid_set's rmse: 33.3908 [8000] valid_set's rmse: 33.3862 [9000] valid_set's rmse: 33.3645 [10000] valid_set's rmse: 33.3686 [1000] valid_set's rmse: 38.1752 [2000] valid_set's rmse: 36.5188 [3000] valid_set's rmse: 36.1264 [4000] valid_set's rmse: 35.9954 [5000] valid_set's rmse: 35.9337 [6000] valid_set's rmse: 35.9463 [1000] valid_set's rmse: 38.9031 [2000] valid_set's rmse: 36.7896 [3000] valid_set's rmse: 36.3287 [4000] valid_set's rmse: 36.2175 [5000] valid_set's rmse: 36.1359 [6000] valid_set's rmse: 36.0948 [7000] valid_set's rmse: 36.174 [1000] valid_set's rmse: 35.8977 [2000] valid_set's rmse: 33.4992 [3000] valid_set's rmse: 32.7907 [4000] valid_set's rmse: 32.4471 [5000] valid_set's rmse: 32.2892 [6000] valid_set's rmse: 32.2846 [7000] valid_set's rmse: 32.2649 [8000] valid_set's rmse: 32.3084 [1000] valid_set's rmse: 38.3394 [2000] valid_set's rmse: 37.1199 [3000] valid_set's rmse: 36.8417 [4000] valid_set's rmse: 36.6798 [5000] valid_set's rmse: 36.6466 [6000] valid_set's rmse: 36.6288 [7000] valid_set's rmse: 36.6832 [1000] valid_set's rmse: 35.8969 [2000] valid_set's rmse: 34.1606 [3000] valid_set's rmse: 33.8527 [4000] valid_set's rmse: 33.714 [5000] valid_set's rmse: 33.6917
-34.4539 = Validation score (-root_mean_squared_error) 73.51s = Training runtime 11.95s = Validation runtime Fitting model: LightGBM_BAG_L1 ... Training model for up to 302.83s of the 502.6s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000] valid_set's rmse: 33.1713 [2000] valid_set's rmse: 33.0077 [1000] valid_set's rmse: 32.8635 [2000] valid_set's rmse: 32.6404 [1000] valid_set's rmse: 31.9543 [2000] valid_set's rmse: 31.343 [3000] valid_set's rmse: 30.9039 [4000] valid_set's rmse: 30.8612 [1000] valid_set's rmse: 35.8483 [2000] valid_set's rmse: 35.4773 [3000] valid_set's rmse: 35.3993 [1000] valid_set's rmse: 35.5388 [1000] valid_set's rmse: 31.6283 [1000] valid_set's rmse: 37.9327 [2000] valid_set's rmse: 37.4577 [1000] valid_set's rmse: 34.9434 [2000] valid_set's rmse: 34.6719
-33.9173 = Validation score (-root_mean_squared_error) 25.25s = Training runtime 3.05s = Validation runtime Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 271.09s of the 470.86s of remaining time. -38.425 = Validation score (-root_mean_squared_error) 16.73s = Training runtime 0.57s = Validation runtime Fitting model: CatBoost_BAG_L1 ... Training model for up to 253.37s of the 453.14s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy Ran out of time, early stopping on iteration 2589. Ran out of time, early stopping on iteration 2730. Ran out of time, early stopping on iteration 2714. Ran out of time, early stopping on iteration 2814. Ran out of time, early stopping on iteration 3062. Ran out of time, early stopping on iteration 3113. Ran out of time, early stopping on iteration 3355. Ran out of time, early stopping on iteration 3689. -34.056 = Validation score (-root_mean_squared_error) 243.0s = Training runtime 0.1s = Validation runtime Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 10.15s of the 209.91s of remaining time. -38.1073 = Validation score (-root_mean_squared_error) 8.32s = Training runtime 0.55s = Validation runtime Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 0.81s of the 200.58s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy Time limit exceeded... Skipping NeuralNetFastAI_BAG_L1. Fitting model: XGBoost_BAG_L1 ... Training model for up to 0.57s of the 200.33s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy Time limit exceeded... Skipping XGBoost_BAG_L1. Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 0.44s of the 200.21s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy Time limit exceeded... Skipping NeuralNetTorch_BAG_L1. Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 0.34s of the 200.11s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy Ran out of time, early stopping on iteration 1. Best iteration is: [1] valid_set's rmse: 176.729 Time limit exceeded... Skipping LightGBMLarge_BAG_L1. Completed 1/20 k-fold bagging repeats ... Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 199.73s of remaining time. -32.1785 = Validation score (-root_mean_squared_error) 0.39s = Training runtime 0.0s = Validation runtime Fitting 9 L2 models ... Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 199.32s of the 199.3s of remaining time. Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
[1000] valid_set's rmse: 30.095 [1000] valid_set's rmse: 30.9622
-31.1534 = Validation score (-root_mean_squared_error)
15.13s = Training runtime
0.71s = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 182.32s of the 182.3s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
-30.6569 = Validation score (-root_mean_squared_error)
10.26s = Training runtime
0.26s = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 171.42s of the 171.41s of remaining time.
-31.678 = Validation score (-root_mean_squared_error)
40.14s = Training runtime
0.61s = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 130.24s of the 130.22s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Ran out of time, early stopping on iteration 1275.
Ran out of time, early stopping on iteration 1242.
-30.4785 = Validation score (-root_mean_squared_error)
120.83s = Training runtime
0.06s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 9.28s of the 9.26s of remaining time.
-31.4883 = Validation score (-root_mean_squared_error)
12.32s = Training runtime
0.75s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -4.37s of remaining time.
-30.2294 = Validation score (-root_mean_squared_error)
0.32s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 604.72s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20240430_034236")
In [52]:
predictor_new_features.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -30.229371 17.975116 553.563776 0.000632 0.319943 3 True 14
1 CatBoost_BAG_L2 -30.478472 16.387324 487.721086 0.057159 120.831526 2 True 12
2 LightGBM_BAG_L2 -30.656909 16.592012 377.147769 0.261847 10.258209 2 True 10
3 LightGBMXT_BAG_L2 -31.153360 17.044061 382.018803 0.713896 15.129243 2 True 9
4 ExtraTreesMSE_BAG_L2 -31.488288 17.083068 379.207504 0.752903 12.317944 2 True 13
5 RandomForestMSE_BAG_L2 -31.678040 16.941582 407.024855 0.611417 40.135296 2 True 11
6 WeightedEnsemble_L2 -32.178548 15.721569 358.911448 0.000759 0.393991 2 True 8
7 LightGBM_BAG_L1 -33.917339 3.049416 25.245680 3.049416 25.245680 1 True 4
8 CatBoost_BAG_L1 -34.056005 0.100985 242.995549 0.100985 242.995549 1 True 6
9 LightGBMXT_BAG_L1 -34.453884 11.953281 73.513387 11.953281 73.513387 1 True 3
10 ExtraTreesMSE_BAG_L1 -38.107278 0.553487 8.316738 0.553487 8.316738 1 True 7
11 RandomForestMSE_BAG_L1 -38.424984 0.565053 16.730583 0.565053 16.730583 1 True 5
12 KNeighborsDist_BAG_L1 -84.125061 0.052075 0.032257 0.052075 0.032257 1 True 2
13 KNeighborsUnif_BAG_L1 -101.546199 0.055868 0.055365 0.055868 0.055365 1 True 1
Number of models trained: 14
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'WeightedEnsembleModel', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_RF'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
*** End of fit() summary ***
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
Out[52]:
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
'KNeighborsDist_BAG_L1': -84.12506123181602,
'LightGBMXT_BAG_L1': -34.453884062670745,
'LightGBM_BAG_L1': -33.91733862651761,
'RandomForestMSE_BAG_L1': -38.424983594881716,
'CatBoost_BAG_L1': -34.05600453907308,
'ExtraTreesMSE_BAG_L1': -38.10727767243523,
'WeightedEnsemble_L2': -32.17854848587382,
'LightGBMXT_BAG_L2': -31.153360362923692,
'LightGBM_BAG_L2': -30.656909399225658,
'RandomForestMSE_BAG_L2': -31.67804049001475,
'CatBoost_BAG_L2': -30.47847179918766,
'ExtraTreesMSE_BAG_L2': -31.488287938277107,
'WeightedEnsemble_L3': -30.229370529662308},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'KNeighborsUnif_BAG_L1': ['KNeighborsUnif_BAG_L1'],
'KNeighborsDist_BAG_L1': ['KNeighborsDist_BAG_L1'],
'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],
'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],
'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],
'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],
'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],
'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],
'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],
'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],
'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],
'CatBoost_BAG_L2': ['CatBoost_BAG_L2'],
'ExtraTreesMSE_BAG_L2': ['ExtraTreesMSE_BAG_L2'],
'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},
'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.05536460876464844,
'KNeighborsDist_BAG_L1': 0.032257080078125,
'LightGBMXT_BAG_L1': 73.51338744163513,
'LightGBM_BAG_L1': 25.2456796169281,
'RandomForestMSE_BAG_L1': 16.73058319091797,
'CatBoost_BAG_L1': 242.99554920196533,
'ExtraTreesMSE_BAG_L1': 8.316738367080688,
'WeightedEnsemble_L2': 0.39399123191833496,
'LightGBMXT_BAG_L2': 15.12924313545227,
'LightGBM_BAG_L2': 10.258209228515625,
'RandomForestMSE_BAG_L2': 40.13529586791992,
'CatBoost_BAG_L2': 120.83152604103088,
'ExtraTreesMSE_BAG_L2': 12.317944288253784,
'WeightedEnsemble_L3': 0.3199427127838135},
'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.055867910385131836,
'KNeighborsDist_BAG_L1': 0.05207467079162598,
'LightGBMXT_BAG_L1': 11.95328140258789,
'LightGBM_BAG_L1': 3.0494155883789062,
'RandomForestMSE_BAG_L1': 0.5650532245635986,
'CatBoost_BAG_L1': 0.10098505020141602,
'ExtraTreesMSE_BAG_L1': 0.5534873008728027,
'WeightedEnsemble_L2': 0.0007586479187011719,
'LightGBMXT_BAG_L2': 0.7138962745666504,
'LightGBM_BAG_L2': 0.26184725761413574,
'RandomForestMSE_BAG_L2': 0.6114168167114258,
'CatBoost_BAG_L2': 0.057158708572387695,
'ExtraTreesMSE_BAG_L2': 0.7529025077819824,
'WeightedEnsemble_L3': 0.0006318092346191406},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'KNeighborsDist_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'LightGBMXT_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMXT_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -30.229371 17.975116 553.563776
1 CatBoost_BAG_L2 -30.478472 16.387324 487.721086
2 LightGBM_BAG_L2 -30.656909 16.592012 377.147769
3 LightGBMXT_BAG_L2 -31.153360 17.044061 382.018803
4 ExtraTreesMSE_BAG_L2 -31.488288 17.083068 379.207504
5 RandomForestMSE_BAG_L2 -31.678040 16.941582 407.024855
6 WeightedEnsemble_L2 -32.178548 15.721569 358.911448
7 LightGBM_BAG_L1 -33.917339 3.049416 25.245680
8 CatBoost_BAG_L1 -34.056005 0.100985 242.995549
9 LightGBMXT_BAG_L1 -34.453884 11.953281 73.513387
10 ExtraTreesMSE_BAG_L1 -38.107278 0.553487 8.316738
11 RandomForestMSE_BAG_L1 -38.424984 0.565053 16.730583
12 KNeighborsDist_BAG_L1 -84.125061 0.052075 0.032257
13 KNeighborsUnif_BAG_L1 -101.546199 0.055868 0.055365
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000632 0.319943 3 True
1 0.057159 120.831526 2 True
2 0.261847 10.258209 2 True
3 0.713896 15.129243 2 True
4 0.752903 12.317944 2 True
5 0.611417 40.135296 2 True
6 0.000759 0.393991 2 True
7 3.049416 25.245680 1 True
8 0.100985 242.995549 1 True
9 11.953281 73.513387 1 True
10 0.553487 8.316738 1 True
11 0.565053 16.730583 1 True
12 0.052075 0.032257 1 True
13 0.055868 0.055365 1 True
fit_order
0 14
1 12
2 10
3 9
4 13
5 11
6 8
7 4
8 6
9 3
10 7
11 5
12 2
13 1 }
In [53]:
predictions_new_features = predictor_new_features.predict(test)
predictions_new_features = {'datetime': test['datetime'], 'Pred_count': predictions_new_features}
predictions_new_features = pd.DataFrame(data=predictions_new_features)
predictions_new_features.head()
Out[53]:
| datetime | Pred_count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 16.358873 |
| 1 | 2011-01-20 01:00:00 | 11.384792 |
| 2 | 2011-01-20 02:00:00 | 10.392972 |
| 3 | 2011-01-20 03:00:00 | 9.292883 |
| 4 | 2011-01-20 04:00:00 | 7.534829 |
In [54]:
predictions_new_features[predictions_new_features['Pred_count']<0] = 0
In [55]:
predictions_new_features.describe()
Out[55]:
| datetime | Pred_count | |
|---|---|---|
| count | 6493 | 6493.000000 |
| mean | 2012-01-13 09:27:47.765285632 | 153.902969 |
| min | 2011-01-20 00:00:00 | 1.313361 |
| 25% | 2011-07-22 15:00:00 | 54.035172 |
| 50% | 2012-01-20 23:00:00 | 119.526962 |
| 75% | 2012-07-20 17:00:00 | 218.603973 |
| max | 2012-12-31 23:00:00 | 820.556641 |
| std | NaN | 132.769485 |
In [56]:
# Same submitting predictions
submission_new_features = pd.read_csv('submission.csv')
submission_new_features["count"] = predictions_new_features['Pred_count']
submission_new_features.to_csv("submission_new_features.csv", index=False)
In [44]:
!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m "new features"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 576kB/s] Successfully submitted to Bike Sharing Demand
In [45]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- -------------------- -------- ----------- ------------ submission_new_features.csv 2024-04-30 02:37:04 new features pending submission.csv 2024-04-30 02:20:52 first raw submission complete 1.80512 1.80512 submission_new_features.csv 2024-04-28 22:14:56 new features complete 0.65798 0.65798 submission.csv 2024-04-28 22:02:47 first raw submission complete 1.84007 1.84007
In [ ]:
In [57]:
import autogluon.core as ag
nn_options = { # specifies non-default hyperparameter values for neural network models
'num_epochs': 10, # number of training epochs (controls training time of NN models)
'learning_rate': ag.space.Real(1e-4, 1e-2, default=5e-4, log=True), # learning rate used in training (real-valued hyperparameter searched on log-scale)
'activation': ag.space.Categorical('relu', 'softrelu', 'tanh'), # activation function used in NN (categorical hyperparameter, default = first entry)
'layers': ag.space.Categorical([100], [1000], [200, 100], [300, 200, 100]), # each choice for categorical hyperparameter 'layers' corresponds to list of sizes for each NN layer to use
'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1), # dropout probability (real-valued hyperparameter)
}
gbm_options = { # specifies non-default hyperparameter values for lightGBM gradient boosted trees
'num_boost_round': 100, # number of boosting rounds (controls training time of GBM models)
'num_leaves': ag.space.Int(lower=26, upper=66, default=36), # number of leaves in trees (integer hyperparameter)
}
hyperparameters = { # hyperparameters of each model type
'GBM': gbm_options,
#'NN': nn_options, # NOTE: comment this line out if you get errors on Mac OSX
} # When these keys are missing from hyperparameters dict, no models of that type are trained
#num_trials = 5 # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto' # to tune hyperparameters using Bayesian optimization routine with a local scheduler
hyperparameter_tune_kwargs = { # HPO is not performed unless hyperparameter_tune_kwargs is specified
#'num_trials': num_trials,
'scheduler' : 'local',
'searcher': search_strategy,
}
predictor_new_hpo = TabularPredictor(label="count", eval_metric="root_mean_squared_error",learner_kwargs={"ignored_columns":
["casual", "registered"]}).fit(train_data=train, time_limit=600, presets="best_quality", hyperparameters=hyperparameters, hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
)
No path specified. Models will be saved in: "AutogluonModels/ag-20240430_035402"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/utils.py:564: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context("mode.use_inf_as_na", True): # treat None, NaN, INF, NINF as NA
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20240430_035402"
AutoGluon Version: 0.8.2
Python Version: 3.10.14
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Sat Mar 23 09:49:55 UTC 2024
Disk Space Avail: 2.47 GB / 5.36 GB (46.1%)
WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception.
We recommend a minimum available disk space of 10 GB, and large datasets may require more.
Train Data Rows: 10886
Train Data Columns: 15
Label Column: count
Preprocessing data ...
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/utils.py:564: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context("mode.use_inf_as_na", True): # treat None, NaN, INF, NINF as NA
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
If 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
/opt/conda/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context("mode.use_inf_as_na", True): # treat None, NaN, INF, NINF as NA
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 2249.49 MB
Train Data (Original) Memory Usage: 0.81 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
0.7s = Fit runtime
13 features in original data used to generate 15 features in processed data.
Train Data (Processed) Memory Usage: 0.8 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.81s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
User-specified model hyperparameters to be fit:
{
'GBM': {'num_boost_round': 100, 'num_leaves': Int: lower=26, upper=66},
}
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 1 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 359.43s of the 599.19s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
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Ran out of time, early stopping on iteration 59. Best iteration is:
[59] valid_set's rmse: 143.534
Ran out of time, early stopping on iteration 60. Best iteration is:
[60] valid_set's rmse: 147.596
Ran out of time, early stopping on iteration 86. Best iteration is:
[86] valid_set's rmse: 133.845
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Ran out of time, early stopping on iteration 1. Best iteration is:
[1] valid_set's rmse: 167.609
Stopping HPO to satisfy time limit...
Fitted model: LightGBM_BAG_L1/T1 ...
-40.2554 = Validation score (-root_mean_squared_error)
4.24s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T2 ...
-39.2133 = Validation score (-root_mean_squared_error)
4.24s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T3 ...
-38.2356 = Validation score (-root_mean_squared_error)
5.09s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T4 ...
-122.1122 = Validation score (-root_mean_squared_error)
4.24s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T5 ...
-43.2684 = Validation score (-root_mean_squared_error)
4.31s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T6 ...
-109.5652 = Validation score (-root_mean_squared_error)
4.44s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T7 ...
-38.9229 = Validation score (-root_mean_squared_error)
4.38s = Training runtime
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Fitted model: LightGBM_BAG_L1/T8 ...
-36.344 = Validation score (-root_mean_squared_error)
4.11s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T9 ...
-107.8719 = Validation score (-root_mean_squared_error)
3.8s = Training runtime
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Fitted model: LightGBM_BAG_L1/T10 ...
-36.2276 = Validation score (-root_mean_squared_error)
3.98s = Training runtime
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Fitted model: LightGBM_BAG_L1/T11 ...
-75.4893 = Validation score (-root_mean_squared_error)
4.9s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T12 ...
-125.1145 = Validation score (-root_mean_squared_error)
4.23s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T13 ...
-41.4674 = Validation score (-root_mean_squared_error)
4.25s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T14 ...
-66.1791 = Validation score (-root_mean_squared_error)
4.22s = Training runtime
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Fitted model: LightGBM_BAG_L1/T15 ...
-110.3224 = Validation score (-root_mean_squared_error)
4.53s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T16 ...
-92.7813 = Validation score (-root_mean_squared_error)
4.34s = Training runtime
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Fitted model: LightGBM_BAG_L1/T17 ...
-61.6928 = Validation score (-root_mean_squared_error)
4.49s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T18 ...
-37.2716 = Validation score (-root_mean_squared_error)
4.2s = Training runtime
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Fitted model: LightGBM_BAG_L1/T19 ...
-40.9873 = Validation score (-root_mean_squared_error)
4.61s = Training runtime
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Fitted model: LightGBM_BAG_L1/T20 ...
-39.7361 = Validation score (-root_mean_squared_error)
4.65s = Training runtime
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Fitted model: LightGBM_BAG_L1/T21 ...
-63.5598 = Validation score (-root_mean_squared_error)
4.6s = Training runtime
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Fitted model: LightGBM_BAG_L1/T22 ...
-35.4298 = Validation score (-root_mean_squared_error)
4.75s = Training runtime
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Fitted model: LightGBM_BAG_L1/T23 ...
-35.9882 = Validation score (-root_mean_squared_error)
4.62s = Training runtime
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Fitted model: LightGBM_BAG_L1/T24 ...
-54.9086 = Validation score (-root_mean_squared_error)
4.72s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T25 ...
-111.0101 = Validation score (-root_mean_squared_error)
4.44s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T26 ...
-37.176 = Validation score (-root_mean_squared_error)
4.41s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T27 ...
-40.0321 = Validation score (-root_mean_squared_error)
5.29s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T28 ...
-36.6122 = Validation score (-root_mean_squared_error)
4.24s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T29 ...
-125.9376 = Validation score (-root_mean_squared_error)
4.55s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T30 ...
-67.2486 = Validation score (-root_mean_squared_error)
4.99s = Training runtime
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Fitted model: LightGBM_BAG_L1/T31 ...
-104.1926 = Validation score (-root_mean_squared_error)
4.36s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T32 ...
-67.7047 = Validation score (-root_mean_squared_error)
4.83s = Training runtime
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Fitted model: LightGBM_BAG_L1/T33 ...
-60.0798 = Validation score (-root_mean_squared_error)
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Fitted model: LightGBM_BAG_L1/T34 ...
-35.1498 = Validation score (-root_mean_squared_error)
4.53s = Training runtime
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Fitted model: LightGBM_BAG_L1/T35 ...
-37.9905 = Validation score (-root_mean_squared_error)
4.34s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T36 ...
-37.3711 = Validation score (-root_mean_squared_error)
4.7s = Training runtime
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Fitted model: LightGBM_BAG_L1/T37 ...
-35.317 = Validation score (-root_mean_squared_error)
4.29s = Training runtime
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Fitted model: LightGBM_BAG_L1/T38 ...
-55.3058 = Validation score (-root_mean_squared_error)
4.17s = Training runtime
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Fitted model: LightGBM_BAG_L1/T39 ...
-38.5032 = Validation score (-root_mean_squared_error)
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Fitted model: LightGBM_BAG_L1/T40 ...
-52.1212 = Validation score (-root_mean_squared_error)
4.94s = Training runtime
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Fitted model: LightGBM_BAG_L1/T41 ...
-45.2744 = Validation score (-root_mean_squared_error)
3.93s = Training runtime
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Fitted model: LightGBM_BAG_L1/T42 ...
-40.6689 = Validation score (-root_mean_squared_error)
4.19s = Training runtime
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Fitted model: LightGBM_BAG_L1/T43 ...
-100.128 = Validation score (-root_mean_squared_error)
4.93s = Training runtime
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Fitted model: LightGBM_BAG_L1/T44 ...
-74.8522 = Validation score (-root_mean_squared_error)
4.07s = Training runtime
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Fitted model: LightGBM_BAG_L1/T45 ...
-110.1831 = Validation score (-root_mean_squared_error)
4.24s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T46 ...
-35.2167 = Validation score (-root_mean_squared_error)
4.58s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T47 ...
-39.7212 = Validation score (-root_mean_squared_error)
4.2s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T48 ...
-103.0587 = Validation score (-root_mean_squared_error)
3.74s = Training runtime
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Fitted model: LightGBM_BAG_L1/T49 ...
-56.1877 = Validation score (-root_mean_squared_error)
4.41s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T50 ...
-66.9086 = Validation score (-root_mean_squared_error)
4.39s = Training runtime
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Fitted model: LightGBM_BAG_L1/T51 ...
-35.7845 = Validation score (-root_mean_squared_error)
4.62s = Training runtime
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Fitted model: LightGBM_BAG_L1/T52 ...
-47.2921 = Validation score (-root_mean_squared_error)
3.96s = Training runtime
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Fitted model: LightGBM_BAG_L1/T53 ...
-122.6625 = Validation score (-root_mean_squared_error)
4.68s = Training runtime
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Fitted model: LightGBM_BAG_L1/T54 ...
-68.1767 = Validation score (-root_mean_squared_error)
4.43s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T55 ...
-36.2983 = Validation score (-root_mean_squared_error)
3.92s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T56 ...
-87.7382 = Validation score (-root_mean_squared_error)
4.29s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T57 ...
-41.4776 = Validation score (-root_mean_squared_error)
4.47s = Training runtime
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Fitted model: LightGBM_BAG_L1/T58 ...
-67.0334 = Validation score (-root_mean_squared_error)
4.32s = Training runtime
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Fitted model: LightGBM_BAG_L1/T59 ...
-99.1388 = Validation score (-root_mean_squared_error)
4.05s = Training runtime
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Fitted model: LightGBM_BAG_L1/T60 ...
-39.0003 = Validation score (-root_mean_squared_error)
4.53s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T61 ...
-48.9962 = Validation score (-root_mean_squared_error)
4.29s = Training runtime
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Fitted model: LightGBM_BAG_L1/T62 ...
-38.7789 = Validation score (-root_mean_squared_error)
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Fitted model: LightGBM_BAG_L1/T63 ...
-36.6609 = Validation score (-root_mean_squared_error)
4.35s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T64 ...
-42.7232 = Validation score (-root_mean_squared_error)
3.53s = Training runtime
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Fitted model: LightGBM_BAG_L1/T65 ...
-35.4953 = Validation score (-root_mean_squared_error)
4.79s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T66 ...
-36.7935 = Validation score (-root_mean_squared_error)
4.95s = Training runtime
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Fitted model: LightGBM_BAG_L1/T67 ...
-64.1554 = Validation score (-root_mean_squared_error)
4.86s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T68 ...
-75.0539 = Validation score (-root_mean_squared_error)
5.11s = Training runtime
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Fitted model: LightGBM_BAG_L1/T69 ...
-40.9699 = Validation score (-root_mean_squared_error)
4.14s = Training runtime
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Fitted model: LightGBM_BAG_L1/T70 ...
-36.9626 = Validation score (-root_mean_squared_error)
4.54s = Training runtime
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Fitted model: LightGBM_BAG_L1/T71 ...
-87.995 = Validation score (-root_mean_squared_error)
4.4s = Training runtime
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Fitted model: LightGBM_BAG_L1/T72 ...
-94.3637 = Validation score (-root_mean_squared_error)
4.4s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T73 ...
-86.6167 = Validation score (-root_mean_squared_error)
3.84s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T74 ...
-104.3449 = Validation score (-root_mean_squared_error)
3.92s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T75 ...
-36.9081 = Validation score (-root_mean_squared_error)
4.36s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T76 ...
-90.7502 = Validation score (-root_mean_squared_error)
4.45s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T77 ...
-61.2038 = Validation score (-root_mean_squared_error)
4.38s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T78 ...
-86.3004 = Validation score (-root_mean_squared_error)
4.45s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T79 ...
-117.0847 = Validation score (-root_mean_squared_error)
4.39s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T80 ...
-129.4395 = Validation score (-root_mean_squared_error)
4.48s = Training runtime
0.0s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 359.99s of the 239.46s of remaining time.
-34.3142 = Validation score (-root_mean_squared_error)
0.54s = Training runtime
0.0s = Validation runtime
Fitting 1 L2 models ...
Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 214.99s of the 238.82s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
Stopping HPO to satisfy time limit...
Fitted model: LightGBM_BAG_L2/T1 ...
-34.1403 = Validation score (-root_mean_squared_error)
16.41s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T2 ...
-34.1614 = Validation score (-root_mean_squared_error)
12.85s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T3 ...
-34.2884 = Validation score (-root_mean_squared_error)
20.69s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T4 ...
-101.9903 = Validation score (-root_mean_squared_error)
13.7s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T5 ...
-34.5473 = Validation score (-root_mean_squared_error)
17.21s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T6 ...
-98.9079 = Validation score (-root_mean_squared_error)
19.6s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T7 ...
-34.0964 = Validation score (-root_mean_squared_error)
11.57s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T8 ...
-34.245 = Validation score (-root_mean_squared_error)
18.52s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T9 ...
-88.9405 = Validation score (-root_mean_squared_error)
14.15s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T10 ...
-34.192 = Validation score (-root_mean_squared_error)
12.91s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T11 ...
-58.0167 = Validation score (-root_mean_squared_error)
15.96s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T12 ...
-109.5844 = Validation score (-root_mean_squared_error)
15.36s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T13 ...
-34.1249 = Validation score (-root_mean_squared_error)
14.19s = Training runtime
0.0s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 35.33s of remaining time.
-33.8989 = Validation score (-root_mean_squared_error)
0.27s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 564.96s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20240430_035402")
In [58]:
predictor_new_hpo.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -33.898886 0.010503 407.617138 0.000579 0.265508 3 True 95
1 LightGBM_BAG_L2/T7 -34.096427 0.009594 365.179346 0.000089 11.569162 2 True 88
2 LightGBM_BAG_L2/T13 -34.124900 0.009602 367.799219 0.000097 14.189035 2 True 94
3 LightGBM_BAG_L2/T1 -34.140293 0.009596 370.022974 0.000090 16.412791 2 True 82
4 LightGBM_BAG_L2/T2 -34.161413 0.009652 366.456404 0.000146 12.846220 2 True 83
5 LightGBM_BAG_L2/T10 -34.192016 0.009598 366.523458 0.000093 12.913274 2 True 91
6 LightGBM_BAG_L2/T8 -34.245007 0.009710 372.134456 0.000205 18.524272 2 True 89
7 LightGBM_BAG_L2/T3 -34.288362 0.009656 374.302429 0.000150 20.692245 2 True 84
8 WeightedEnsemble_L2 -34.314234 0.001973 23.483544 0.001294 0.539096 2 True 81
9 LightGBM_BAG_L2/T5 -34.547295 0.009705 370.820895 0.000199 17.210711 2 True 86
10 LightGBM_BAG_L1/T34 -35.149822 0.000118 4.527263 0.000118 4.527263 1 True 34
11 LightGBM_BAG_L1/T46 -35.216673 0.000098 4.581206 0.000098 4.581206 1 True 46
12 LightGBM_BAG_L1/T37 -35.317043 0.000246 4.293521 0.000246 4.293521 1 True 37
13 LightGBM_BAG_L1/T22 -35.429809 0.000102 4.748456 0.000102 4.748456 1 True 22
14 LightGBM_BAG_L1/T65 -35.495328 0.000115 4.794003 0.000115 4.794003 1 True 65
15 LightGBM_BAG_L1/T51 -35.784493 0.000094 4.622752 0.000094 4.622752 1 True 51
16 LightGBM_BAG_L1/T23 -35.988230 0.000094 4.619236 0.000094 4.619236 1 True 23
17 LightGBM_BAG_L1/T10 -36.227587 0.000094 3.982505 0.000094 3.982505 1 True 10
18 LightGBM_BAG_L1/T55 -36.298321 0.000102 3.918072 0.000102 3.918072 1 True 55
19 LightGBM_BAG_L1/T8 -36.343987 0.000085 4.109572 0.000085 4.109572 1 True 8
20 LightGBM_BAG_L1/T28 -36.612212 0.000119 4.235139 0.000119 4.235139 1 True 28
21 LightGBM_BAG_L1/T63 -36.660925 0.000093 4.346629 0.000093 4.346629 1 True 63
22 LightGBM_BAG_L1/T66 -36.793458 0.000106 4.949643 0.000106 4.949643 1 True 66
23 LightGBM_BAG_L1/T75 -36.908097 0.000104 4.355204 0.000104 4.355204 1 True 75
24 LightGBM_BAG_L1/T70 -36.962596 0.000119 4.537135 0.000119 4.537135 1 True 70
25 LightGBM_BAG_L1/T26 -37.176050 0.000091 4.412049 0.000091 4.412049 1 True 26
26 LightGBM_BAG_L1/T18 -37.271566 0.000111 4.195667 0.000111 4.195667 1 True 18
27 LightGBM_BAG_L1/T36 -37.371118 0.000097 4.698167 0.000097 4.698167 1 True 36
28 LightGBM_BAG_L1/T35 -37.990452 0.000098 4.342577 0.000098 4.342577 1 True 35
29 LightGBM_BAG_L1/T3 -38.235570 0.000106 5.088213 0.000106 5.088213 1 True 3
30 LightGBM_BAG_L1/T39 -38.503157 0.000106 4.596222 0.000106 4.596222 1 True 39
31 LightGBM_BAG_L1/T62 -38.778897 0.000251 4.949531 0.000251 4.949531 1 True 62
32 LightGBM_BAG_L1/T7 -38.922926 0.000089 4.378773 0.000089 4.378773 1 True 7
33 LightGBM_BAG_L1/T60 -39.000336 0.000098 4.526189 0.000098 4.526189 1 True 60
34 LightGBM_BAG_L1/T2 -39.213259 0.000087 4.244906 0.000087 4.244906 1 True 2
35 LightGBM_BAG_L1/T47 -39.721217 0.000091 4.198434 0.000091 4.198434 1 True 47
36 LightGBM_BAG_L1/T20 -39.736145 0.000105 4.650360 0.000105 4.650360 1 True 20
37 LightGBM_BAG_L1/T27 -40.032105 0.000103 5.292323 0.000103 5.292323 1 True 27
38 LightGBM_BAG_L1/T1 -40.255449 0.000103 4.240043 0.000103 4.240043 1 True 1
39 LightGBM_BAG_L1/T42 -40.668913 0.000108 4.188065 0.000108 4.188065 1 True 42
40 LightGBM_BAG_L1/T69 -40.969918 0.000094 4.144487 0.000094 4.144487 1 True 69
41 LightGBM_BAG_L1/T19 -40.987305 0.000106 4.605637 0.000106 4.605637 1 True 19
42 LightGBM_BAG_L1/T13 -41.467370 0.000091 4.251490 0.000091 4.251490 1 True 13
43 LightGBM_BAG_L1/T57 -41.477611 0.000097 4.465062 0.000097 4.465062 1 True 57
44 LightGBM_BAG_L1/T64 -42.723153 0.000102 3.525716 0.000102 3.525716 1 True 64
45 LightGBM_BAG_L1/T5 -43.268413 0.000089 4.307853 0.000089 4.307853 1 True 5
46 LightGBM_BAG_L1/T41 -45.274366 0.000089 3.932689 0.000089 3.932689 1 True 41
47 LightGBM_BAG_L1/T52 -47.292121 0.000120 3.955607 0.000120 3.955607 1 True 52
48 LightGBM_BAG_L1/T61 -48.996161 0.000280 4.292532 0.000280 4.292532 1 True 61
49 LightGBM_BAG_L1/T40 -52.121229 0.000098 4.940718 0.000098 4.940718 1 True 40
50 LightGBM_BAG_L1/T24 -54.908565 0.000091 4.718335 0.000091 4.718335 1 True 24
51 LightGBM_BAG_L1/T38 -55.305840 0.000094 4.170742 0.000094 4.170742 1 True 38
52 LightGBM_BAG_L1/T49 -56.187750 0.000097 4.410849 0.000097 4.410849 1 True 49
53 LightGBM_BAG_L2/T11 -58.016681 0.009601 369.568624 0.000095 15.958440 2 True 92
54 LightGBM_BAG_L1/T33 -60.079767 0.000090 4.523161 0.000090 4.523161 1 True 33
55 LightGBM_BAG_L1/T77 -61.203781 0.000104 4.376895 0.000104 4.376895 1 True 77
56 LightGBM_BAG_L1/T17 -61.692829 0.000343 4.489034 0.000343 4.489034 1 True 17
57 LightGBM_BAG_L1/T21 -63.559775 0.000092 4.603836 0.000092 4.603836 1 True 21
58 LightGBM_BAG_L1/T67 -64.155367 0.000186 4.858462 0.000186 4.858462 1 True 67
59 LightGBM_BAG_L1/T14 -66.179133 0.000126 4.223377 0.000126 4.223377 1 True 14
60 LightGBM_BAG_L1/T50 -66.908631 0.000101 4.391414 0.000101 4.391414 1 True 50
61 LightGBM_BAG_L1/T58 -67.033404 0.000124 4.319664 0.000124 4.319664 1 True 58
62 LightGBM_BAG_L1/T30 -67.248640 0.000136 4.989897 0.000136 4.989897 1 True 30
63 LightGBM_BAG_L1/T32 -67.704651 0.000353 4.826960 0.000353 4.826960 1 True 32
64 LightGBM_BAG_L1/T54 -68.176691 0.000205 4.430502 0.000205 4.430502 1 True 54
65 LightGBM_BAG_L1/T44 -74.852164 0.000098 4.073867 0.000098 4.073867 1 True 44
66 LightGBM_BAG_L1/T68 -75.053871 0.000134 5.106324 0.000134 5.106324 1 True 68
67 LightGBM_BAG_L1/T11 -75.489342 0.000092 4.896541 0.000092 4.896541 1 True 11
68 LightGBM_BAG_L1/T78 -86.300428 0.000087 4.446325 0.000087 4.446325 1 True 78
69 LightGBM_BAG_L1/T73 -86.616737 0.000096 3.838663 0.000096 3.838663 1 True 73
70 LightGBM_BAG_L1/T56 -87.738230 0.000088 4.285486 0.000088 4.285486 1 True 56
71 LightGBM_BAG_L1/T71 -87.995021 0.000102 4.396511 0.000102 4.396511 1 True 71
72 LightGBM_BAG_L2/T9 -88.940490 0.009602 367.759040 0.000096 14.148857 2 True 90
73 LightGBM_BAG_L1/T76 -90.750232 0.000107 4.445532 0.000107 4.445532 1 True 76
74 LightGBM_BAG_L1/T16 -92.781290 0.000100 4.344057 0.000100 4.344057 1 True 16
75 LightGBM_BAG_L1/T72 -94.363749 0.000122 4.395237 0.000122 4.395237 1 True 72
76 LightGBM_BAG_L2/T6 -98.907938 0.009645 373.213178 0.000140 19.602994 2 True 87
77 LightGBM_BAG_L1/T59 -99.138833 0.000087 4.045690 0.000087 4.045690 1 True 59
78 LightGBM_BAG_L1/T43 -100.128027 0.000109 4.929899 0.000109 4.929899 1 True 43
79 LightGBM_BAG_L2/T4 -101.990278 0.010666 367.313714 0.001160 13.703530 2 True 85
80 LightGBM_BAG_L1/T48 -103.058724 0.000088 3.743199 0.000088 3.743199 1 True 48
81 LightGBM_BAG_L1/T31 -104.192597 0.000112 4.362969 0.000112 4.362969 1 True 31
82 LightGBM_BAG_L1/T74 -104.344855 0.000115 3.915456 0.000115 3.915456 1 True 74
83 LightGBM_BAG_L1/T9 -107.871924 0.000101 3.799279 0.000101 3.799279 1 True 9
84 LightGBM_BAG_L1/T6 -109.565161 0.000120 4.436033 0.000120 4.436033 1 True 6
85 LightGBM_BAG_L2/T12 -109.584386 0.009593 368.969010 0.000087 15.358827 2 True 93
86 LightGBM_BAG_L1/T45 -110.183062 0.000175 4.238026 0.000175 4.238026 1 True 45
87 LightGBM_BAG_L1/T15 -110.322411 0.000110 4.532952 0.000110 4.532952 1 True 15
88 LightGBM_BAG_L1/T25 -111.010055 0.000089 4.442690 0.000089 4.442690 1 True 25
89 LightGBM_BAG_L1/T79 -117.084696 0.000098 4.388314 0.000098 4.388314 1 True 79
90 LightGBM_BAG_L1/T4 -122.112198 0.000085 4.237734 0.000085 4.237734 1 True 4
91 LightGBM_BAG_L1/T53 -122.662496 0.000104 4.676295 0.000104 4.676295 1 True 53
92 LightGBM_BAG_L1/T12 -125.114496 0.000091 4.229833 0.000091 4.229833 1 True 12
93 LightGBM_BAG_L1/T29 -125.937609 0.000221 4.547218 0.000221 4.547218 1 True 29
94 LightGBM_BAG_L1/T80 -129.439519 0.000129 4.479278 0.000129 4.479278 1 True 80
Number of models trained: 95
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']
*** End of fit() summary ***
/opt/conda/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
Out[58]:
{'model_types': {'LightGBM_BAG_L1/T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T3': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T4': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T5': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T6': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T7': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T8': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T9': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T10': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T11': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T12': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T13': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T14': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T15': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T16': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T17': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T18': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T19': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T20': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T21': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T22': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T23': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T24': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T25': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T26': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T27': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T28': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T29': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T30': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T31': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T32': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T33': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T34': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T35': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T36': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T37': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T38': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T39': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T40': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T41': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T42': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T43': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T44': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T45': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T46': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T47': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T48': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T49': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T50': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T51': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T52': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T53': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T54': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T55': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T56': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T57': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T58': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T59': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T60': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T61': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T62': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T63': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T64': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T65': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T66': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T67': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T68': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T69': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T70': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T71': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T72': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T73': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T74': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T75': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T76': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T77': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T78': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T79': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T80': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBM_BAG_L2/T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T3': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T4': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T5': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T6': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T7': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T8': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T9': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T10': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T11': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T12': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T13': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'LightGBM_BAG_L1/T1': -40.255448619289915,
'LightGBM_BAG_L1/T2': -39.213258999645646,
'LightGBM_BAG_L1/T3': -38.23556976473342,
'LightGBM_BAG_L1/T4': -122.11219756067042,
'LightGBM_BAG_L1/T5': -43.26841303192551,
'LightGBM_BAG_L1/T6': -109.56516071183998,
'LightGBM_BAG_L1/T7': -38.92292646653962,
'LightGBM_BAG_L1/T8': -36.34398653040369,
'LightGBM_BAG_L1/T9': -107.87192403470239,
'LightGBM_BAG_L1/T10': -36.227586894672875,
'LightGBM_BAG_L1/T11': -75.48934220797054,
'LightGBM_BAG_L1/T12': -125.11449594845834,
'LightGBM_BAG_L1/T13': -41.46737021004442,
'LightGBM_BAG_L1/T14': -66.17913287657221,
'LightGBM_BAG_L1/T15': -110.32241088996467,
'LightGBM_BAG_L1/T16': -92.78128995015007,
'LightGBM_BAG_L1/T17': -61.69282927558979,
'LightGBM_BAG_L1/T18': -37.27156582907592,
'LightGBM_BAG_L1/T19': -40.98730528087555,
'LightGBM_BAG_L1/T20': -39.73614543593702,
'LightGBM_BAG_L1/T21': -63.559775057766394,
'LightGBM_BAG_L1/T22': -35.42980889901937,
'LightGBM_BAG_L1/T23': -35.9882297921665,
'LightGBM_BAG_L1/T24': -54.90856464641669,
'LightGBM_BAG_L1/T25': -111.01005508906172,
'LightGBM_BAG_L1/T26': -37.176049689052626,
'LightGBM_BAG_L1/T27': -40.032104882894814,
'LightGBM_BAG_L1/T28': -36.61221217042727,
'LightGBM_BAG_L1/T29': -125.937608816907,
'LightGBM_BAG_L1/T30': -67.2486395772891,
'LightGBM_BAG_L1/T31': -104.19259731826092,
'LightGBM_BAG_L1/T32': -67.70465133015458,
'LightGBM_BAG_L1/T33': -60.079767357924105,
'LightGBM_BAG_L1/T34': -35.149822410680365,
'LightGBM_BAG_L1/T35': -37.990451931898036,
'LightGBM_BAG_L1/T36': -37.37111766822345,
'LightGBM_BAG_L1/T37': -35.317042834910715,
'LightGBM_BAG_L1/T38': -55.30583968339698,
'LightGBM_BAG_L1/T39': -38.50315702968034,
'LightGBM_BAG_L1/T40': -52.12122913225924,
'LightGBM_BAG_L1/T41': -45.27436556632836,
'LightGBM_BAG_L1/T42': -40.668913497247416,
'LightGBM_BAG_L1/T43': -100.12802686038609,
'LightGBM_BAG_L1/T44': -74.85216417136044,
'LightGBM_BAG_L1/T45': -110.1830618948282,
'LightGBM_BAG_L1/T46': -35.21667306620509,
'LightGBM_BAG_L1/T47': -39.721217397974165,
'LightGBM_BAG_L1/T48': -103.05872423774156,
'LightGBM_BAG_L1/T49': -56.18774957897235,
'LightGBM_BAG_L1/T50': -66.90863120156216,
'LightGBM_BAG_L1/T51': -35.784492646992994,
'LightGBM_BAG_L1/T52': -47.29212086814797,
'LightGBM_BAG_L1/T53': -122.66249551011755,
'LightGBM_BAG_L1/T54': -68.1766905794483,
'LightGBM_BAG_L1/T55': -36.29832054659419,
'LightGBM_BAG_L1/T56': -87.73822993514536,
'LightGBM_BAG_L1/T57': -41.47761058864393,
'LightGBM_BAG_L1/T58': -67.03340397848923,
'LightGBM_BAG_L1/T59': -99.1388328111989,
'LightGBM_BAG_L1/T60': -39.00033572332812,
'LightGBM_BAG_L1/T61': -48.99616096267388,
'LightGBM_BAG_L1/T62': -38.77889721076665,
'LightGBM_BAG_L1/T63': -36.660925345082724,
'LightGBM_BAG_L1/T64': -42.723153097325685,
'LightGBM_BAG_L1/T65': -35.49532831933309,
'LightGBM_BAG_L1/T66': -36.793457898655774,
'LightGBM_BAG_L1/T67': -64.15536691123464,
'LightGBM_BAG_L1/T68': -75.05387068377657,
'LightGBM_BAG_L1/T69': -40.969917645368916,
'LightGBM_BAG_L1/T70': -36.962595941642114,
'LightGBM_BAG_L1/T71': -87.99502123312813,
'LightGBM_BAG_L1/T72': -94.36374923901413,
'LightGBM_BAG_L1/T73': -86.61673655644546,
'LightGBM_BAG_L1/T74': -104.34485546217878,
'LightGBM_BAG_L1/T75': -36.908097162124854,
'LightGBM_BAG_L1/T76': -90.75023167669407,
'LightGBM_BAG_L1/T77': -61.2037807850615,
'LightGBM_BAG_L1/T78': -86.30042761751068,
'LightGBM_BAG_L1/T79': -117.0846955084417,
'LightGBM_BAG_L1/T80': -129.43951877809147,
'WeightedEnsemble_L2': -34.31423439471698,
'LightGBM_BAG_L2/T1': -34.14029252527811,
'LightGBM_BAG_L2/T2': -34.16141314086803,
'LightGBM_BAG_L2/T3': -34.28836161177525,
'LightGBM_BAG_L2/T4': -101.99027789066632,
'LightGBM_BAG_L2/T5': -34.54729509955411,
'LightGBM_BAG_L2/T6': -98.90793750015261,
'LightGBM_BAG_L2/T7': -34.09642659825696,
'LightGBM_BAG_L2/T8': -34.24500728072462,
'LightGBM_BAG_L2/T9': -88.94049048096228,
'LightGBM_BAG_L2/T10': -34.19201565356319,
'LightGBM_BAG_L2/T11': -58.01668051164485,
'LightGBM_BAG_L2/T12': -109.58438636695975,
'LightGBM_BAG_L2/T13': -34.12489987081852,
'WeightedEnsemble_L3': -33.89888612553612},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'LightGBM_BAG_L1/T1': ['LightGBM_BAG_L1', 'T1'],
'LightGBM_BAG_L1/T2': ['LightGBM_BAG_L1', 'T2'],
'LightGBM_BAG_L1/T3': ['LightGBM_BAG_L1', 'T3'],
'LightGBM_BAG_L1/T4': ['LightGBM_BAG_L1', 'T4'],
'LightGBM_BAG_L1/T5': ['LightGBM_BAG_L1', 'T5'],
'LightGBM_BAG_L1/T6': ['LightGBM_BAG_L1', 'T6'],
'LightGBM_BAG_L1/T7': ['LightGBM_BAG_L1', 'T7'],
'LightGBM_BAG_L1/T8': ['LightGBM_BAG_L1', 'T8'],
'LightGBM_BAG_L1/T9': ['LightGBM_BAG_L1', 'T9'],
'LightGBM_BAG_L1/T10': ['LightGBM_BAG_L1', 'T10'],
'LightGBM_BAG_L1/T11': ['LightGBM_BAG_L1', 'T11'],
'LightGBM_BAG_L1/T12': ['LightGBM_BAG_L1', 'T12'],
'LightGBM_BAG_L1/T13': ['LightGBM_BAG_L1', 'T13'],
'LightGBM_BAG_L1/T14': ['LightGBM_BAG_L1', 'T14'],
'LightGBM_BAG_L1/T15': ['LightGBM_BAG_L1', 'T15'],
'LightGBM_BAG_L1/T16': ['LightGBM_BAG_L1', 'T16'],
'LightGBM_BAG_L1/T17': ['LightGBM_BAG_L1', 'T17'],
'LightGBM_BAG_L1/T18': ['LightGBM_BAG_L1', 'T18'],
'LightGBM_BAG_L1/T19': ['LightGBM_BAG_L1', 'T19'],
'LightGBM_BAG_L1/T20': ['LightGBM_BAG_L1', 'T20'],
'LightGBM_BAG_L1/T21': ['LightGBM_BAG_L1', 'T21'],
'LightGBM_BAG_L1/T22': ['LightGBM_BAG_L1', 'T22'],
'LightGBM_BAG_L1/T23': ['LightGBM_BAG_L1', 'T23'],
'LightGBM_BAG_L1/T24': ['LightGBM_BAG_L1', 'T24'],
'LightGBM_BAG_L1/T25': ['LightGBM_BAG_L1', 'T25'],
'LightGBM_BAG_L1/T26': ['LightGBM_BAG_L1', 'T26'],
'LightGBM_BAG_L1/T27': ['LightGBM_BAG_L1', 'T27'],
'LightGBM_BAG_L1/T28': ['LightGBM_BAG_L1', 'T28'],
'LightGBM_BAG_L1/T29': ['LightGBM_BAG_L1', 'T29'],
'LightGBM_BAG_L1/T30': ['LightGBM_BAG_L1', 'T30'],
'LightGBM_BAG_L1/T31': ['LightGBM_BAG_L1', 'T31'],
'LightGBM_BAG_L1/T32': ['LightGBM_BAG_L1', 'T32'],
'LightGBM_BAG_L1/T33': ['LightGBM_BAG_L1', 'T33'],
'LightGBM_BAG_L1/T34': ['LightGBM_BAG_L1', 'T34'],
'LightGBM_BAG_L1/T35': ['LightGBM_BAG_L1', 'T35'],
'LightGBM_BAG_L1/T36': ['LightGBM_BAG_L1', 'T36'],
'LightGBM_BAG_L1/T37': ['LightGBM_BAG_L1', 'T37'],
'LightGBM_BAG_L1/T38': ['LightGBM_BAG_L1', 'T38'],
'LightGBM_BAG_L1/T39': ['LightGBM_BAG_L1', 'T39'],
'LightGBM_BAG_L1/T40': ['LightGBM_BAG_L1', 'T40'],
'LightGBM_BAG_L1/T41': ['LightGBM_BAG_L1', 'T41'],
'LightGBM_BAG_L1/T42': ['LightGBM_BAG_L1', 'T42'],
'LightGBM_BAG_L1/T43': ['LightGBM_BAG_L1', 'T43'],
'LightGBM_BAG_L1/T44': ['LightGBM_BAG_L1', 'T44'],
'LightGBM_BAG_L1/T45': ['LightGBM_BAG_L1', 'T45'],
'LightGBM_BAG_L1/T46': ['LightGBM_BAG_L1', 'T46'],
'LightGBM_BAG_L1/T47': ['LightGBM_BAG_L1', 'T47'],
'LightGBM_BAG_L1/T48': ['LightGBM_BAG_L1', 'T48'],
'LightGBM_BAG_L1/T49': ['LightGBM_BAG_L1', 'T49'],
'LightGBM_BAG_L1/T50': ['LightGBM_BAG_L1', 'T50'],
'LightGBM_BAG_L1/T51': ['LightGBM_BAG_L1', 'T51'],
'LightGBM_BAG_L1/T52': ['LightGBM_BAG_L1', 'T52'],
'LightGBM_BAG_L1/T53': ['LightGBM_BAG_L1', 'T53'],
'LightGBM_BAG_L1/T54': ['LightGBM_BAG_L1', 'T54'],
'LightGBM_BAG_L1/T55': ['LightGBM_BAG_L1', 'T55'],
'LightGBM_BAG_L1/T56': ['LightGBM_BAG_L1', 'T56'],
'LightGBM_BAG_L1/T57': ['LightGBM_BAG_L1', 'T57'],
'LightGBM_BAG_L1/T58': ['LightGBM_BAG_L1', 'T58'],
'LightGBM_BAG_L1/T59': ['LightGBM_BAG_L1', 'T59'],
'LightGBM_BAG_L1/T60': ['LightGBM_BAG_L1', 'T60'],
'LightGBM_BAG_L1/T61': ['LightGBM_BAG_L1', 'T61'],
'LightGBM_BAG_L1/T62': ['LightGBM_BAG_L1', 'T62'],
'LightGBM_BAG_L1/T63': ['LightGBM_BAG_L1', 'T63'],
'LightGBM_BAG_L1/T64': ['LightGBM_BAG_L1', 'T64'],
'LightGBM_BAG_L1/T65': ['LightGBM_BAG_L1', 'T65'],
'LightGBM_BAG_L1/T66': ['LightGBM_BAG_L1', 'T66'],
'LightGBM_BAG_L1/T67': ['LightGBM_BAG_L1', 'T67'],
'LightGBM_BAG_L1/T68': ['LightGBM_BAG_L1', 'T68'],
'LightGBM_BAG_L1/T69': ['LightGBM_BAG_L1', 'T69'],
'LightGBM_BAG_L1/T70': ['LightGBM_BAG_L1', 'T70'],
'LightGBM_BAG_L1/T71': ['LightGBM_BAG_L1', 'T71'],
'LightGBM_BAG_L1/T72': ['LightGBM_BAG_L1', 'T72'],
'LightGBM_BAG_L1/T73': ['LightGBM_BAG_L1', 'T73'],
'LightGBM_BAG_L1/T74': ['LightGBM_BAG_L1', 'T74'],
'LightGBM_BAG_L1/T75': ['LightGBM_BAG_L1', 'T75'],
'LightGBM_BAG_L1/T76': ['LightGBM_BAG_L1', 'T76'],
'LightGBM_BAG_L1/T77': ['LightGBM_BAG_L1', 'T77'],
'LightGBM_BAG_L1/T78': ['LightGBM_BAG_L1', 'T78'],
'LightGBM_BAG_L1/T79': ['LightGBM_BAG_L1', 'T79'],
'LightGBM_BAG_L1/T80': ['LightGBM_BAG_L1', 'T80'],
'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],
'LightGBM_BAG_L2/T1': ['LightGBM_BAG_L2', 'T1'],
'LightGBM_BAG_L2/T2': ['LightGBM_BAG_L2', 'T2'],
'LightGBM_BAG_L2/T3': ['LightGBM_BAG_L2', 'T3'],
'LightGBM_BAG_L2/T4': ['LightGBM_BAG_L2', 'T4'],
'LightGBM_BAG_L2/T5': ['LightGBM_BAG_L2', 'T5'],
'LightGBM_BAG_L2/T6': ['LightGBM_BAG_L2', 'T6'],
'LightGBM_BAG_L2/T7': ['LightGBM_BAG_L2', 'T7'],
'LightGBM_BAG_L2/T8': ['LightGBM_BAG_L2', 'T8'],
'LightGBM_BAG_L2/T9': ['LightGBM_BAG_L2', 'T9'],
'LightGBM_BAG_L2/T10': ['LightGBM_BAG_L2', 'T10'],
'LightGBM_BAG_L2/T11': ['LightGBM_BAG_L2', 'T11'],
'LightGBM_BAG_L2/T12': ['LightGBM_BAG_L2', 'T12'],
'LightGBM_BAG_L2/T13': ['LightGBM_BAG_L2', 'T13'],
'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},
'model_fit_times': {'LightGBM_BAG_L1/T1': 4.2400429248809814,
'LightGBM_BAG_L1/T2': 4.244906187057495,
'LightGBM_BAG_L1/T3': 5.088212728500366,
'LightGBM_BAG_L1/T4': 4.237733840942383,
'LightGBM_BAG_L1/T5': 4.307852745056152,
'LightGBM_BAG_L1/T6': 4.436033248901367,
'LightGBM_BAG_L1/T7': 4.378773212432861,
'LightGBM_BAG_L1/T8': 4.109571933746338,
'LightGBM_BAG_L1/T9': 3.7992794513702393,
'LightGBM_BAG_L1/T10': 3.9825048446655273,
'LightGBM_BAG_L1/T11': 4.896540880203247,
'LightGBM_BAG_L1/T12': 4.229833126068115,
'LightGBM_BAG_L1/T13': 4.251490116119385,
'LightGBM_BAG_L1/T14': 4.223377466201782,
'LightGBM_BAG_L1/T15': 4.532952070236206,
'LightGBM_BAG_L1/T16': 4.344057083129883,
'LightGBM_BAG_L1/T17': 4.489034175872803,
'LightGBM_BAG_L1/T18': 4.195667266845703,
'LightGBM_BAG_L1/T19': 4.605637073516846,
'LightGBM_BAG_L1/T20': 4.650359630584717,
'LightGBM_BAG_L1/T21': 4.603835821151733,
'LightGBM_BAG_L1/T22': 4.748455762863159,
'LightGBM_BAG_L1/T23': 4.6192357540130615,
'LightGBM_BAG_L1/T24': 4.718334913253784,
'LightGBM_BAG_L1/T25': 4.442689657211304,
'LightGBM_BAG_L1/T26': 4.412048816680908,
'LightGBM_BAG_L1/T27': 5.292322635650635,
'LightGBM_BAG_L1/T28': 4.2351393699646,
'LightGBM_BAG_L1/T29': 4.547218084335327,
'LightGBM_BAG_L1/T30': 4.98989725112915,
'LightGBM_BAG_L1/T31': 4.362969160079956,
'LightGBM_BAG_L1/T32': 4.826959609985352,
'LightGBM_BAG_L1/T33': 4.523161172866821,
'LightGBM_BAG_L1/T34': 4.527262926101685,
'LightGBM_BAG_L1/T35': 4.342577219009399,
'LightGBM_BAG_L1/T36': 4.698166847229004,
'LightGBM_BAG_L1/T37': 4.29352068901062,
'LightGBM_BAG_L1/T38': 4.1707422733306885,
'LightGBM_BAG_L1/T39': 4.596221923828125,
'LightGBM_BAG_L1/T40': 4.940717697143555,
'LightGBM_BAG_L1/T41': 3.9326891899108887,
'LightGBM_BAG_L1/T42': 4.188064813613892,
'LightGBM_BAG_L1/T43': 4.929899454116821,
'LightGBM_BAG_L1/T44': 4.073866844177246,
'LightGBM_BAG_L1/T45': 4.238026142120361,
'LightGBM_BAG_L1/T46': 4.581206321716309,
'LightGBM_BAG_L1/T47': 4.198434114456177,
'LightGBM_BAG_L1/T48': 3.743199348449707,
'LightGBM_BAG_L1/T49': 4.410849332809448,
'LightGBM_BAG_L1/T50': 4.391414403915405,
'LightGBM_BAG_L1/T51': 4.622751712799072,
'LightGBM_BAG_L1/T52': 3.9556071758270264,
'LightGBM_BAG_L1/T53': 4.676295042037964,
'LightGBM_BAG_L1/T54': 4.430501699447632,
'LightGBM_BAG_L1/T55': 3.918072462081909,
'LightGBM_BAG_L1/T56': 4.285486459732056,
'LightGBM_BAG_L1/T57': 4.465062379837036,
'LightGBM_BAG_L1/T58': 4.319664001464844,
'LightGBM_BAG_L1/T59': 4.045689582824707,
'LightGBM_BAG_L1/T60': 4.52618932723999,
'LightGBM_BAG_L1/T61': 4.292531728744507,
'LightGBM_BAG_L1/T62': 4.949530601501465,
'LightGBM_BAG_L1/T63': 4.346628665924072,
'LightGBM_BAG_L1/T64': 3.5257155895233154,
'LightGBM_BAG_L1/T65': 4.794002532958984,
'LightGBM_BAG_L1/T66': 4.949642896652222,
'LightGBM_BAG_L1/T67': 4.858461618423462,
'LightGBM_BAG_L1/T68': 5.1063244342803955,
'LightGBM_BAG_L1/T69': 4.144487142562866,
'LightGBM_BAG_L1/T70': 4.537135362625122,
'LightGBM_BAG_L1/T71': 4.3965113162994385,
'LightGBM_BAG_L1/T72': 4.395237445831299,
'LightGBM_BAG_L1/T73': 3.838663339614868,
'LightGBM_BAG_L1/T74': 3.9154555797576904,
'LightGBM_BAG_L1/T75': 4.355203866958618,
'LightGBM_BAG_L1/T76': 4.4455320835113525,
'LightGBM_BAG_L1/T77': 4.37689471244812,
'LightGBM_BAG_L1/T78': 4.446325302124023,
'LightGBM_BAG_L1/T79': 4.388314485549927,
'LightGBM_BAG_L1/T80': 4.479277610778809,
'WeightedEnsemble_L2': 0.5390961170196533,
'LightGBM_BAG_L2/T1': 16.412790536880493,
'LightGBM_BAG_L2/T2': 12.846220254898071,
'LightGBM_BAG_L2/T3': 20.69224500656128,
'LightGBM_BAG_L2/T4': 13.703530073165894,
'LightGBM_BAG_L2/T5': 17.21071147918701,
'LightGBM_BAG_L2/T6': 19.602994203567505,
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'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T5': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T6': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T7': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T8': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T9': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T10': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T11': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T12': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T13': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -33.898886 0.010503 407.617138
1 LightGBM_BAG_L2/T7 -34.096427 0.009594 365.179346
2 LightGBM_BAG_L2/T13 -34.124900 0.009602 367.799219
3 LightGBM_BAG_L2/T1 -34.140293 0.009596 370.022974
4 LightGBM_BAG_L2/T2 -34.161413 0.009652 366.456404
.. ... ... ... ...
90 LightGBM_BAG_L1/T4 -122.112198 0.000085 4.237734
91 LightGBM_BAG_L1/T53 -122.662496 0.000104 4.676295
92 LightGBM_BAG_L1/T12 -125.114496 0.000091 4.229833
93 LightGBM_BAG_L1/T29 -125.937609 0.000221 4.547218
94 LightGBM_BAG_L1/T80 -129.439519 0.000129 4.479278
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000579 0.265508 3 True
1 0.000089 11.569162 2 True
2 0.000097 14.189035 2 True
3 0.000090 16.412791 2 True
4 0.000146 12.846220 2 True
.. ... ... ... ...
90 0.000085 4.237734 1 True
91 0.000104 4.676295 1 True
92 0.000091 4.229833 1 True
93 0.000221 4.547218 1 True
94 0.000129 4.479278 1 True
fit_order
0 95
1 88
2 94
3 82
4 83
.. ...
90 4
91 53
92 12
93 29
94 80
[95 rows x 9 columns]}
In [59]:
new_predictions_hpo = predictor_new_hpo.predict(test)
new_predictions_hpo[new_predictions_hpo<0] = 0
In [60]:
# Same submitting predictions
submission_new_hpo = pd.read_csv("submission.csv", parse_dates=["datetime"])
submission_new_hpo["count"] = new_predictions_hpo
submission_new_hpo.to_csv("submission_new_hpo.csv", index=False)
In [61]:
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m "new features with hyperparameters"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 673kB/s] Successfully submitted to Bike Sharing Demand
In [62]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- --------------------------------- -------- ----------- ------------ submission_new_hpo.csv 2024-04-30 04:04:03 new features with hyperparameters complete 0.47887 0.47887 submission.csv 2024-04-30 03:42:29 first raw submission complete 1.80337 1.80337 submission_new_hpo.csv 2024-04-30 02:53:22 new features with hyperparameters complete 0.48188 0.48188 submission_new_features.csv 2024-04-30 02:37:04 new features complete 0.6741 0.6741
In [63]:
fig = pd.DataFrame(
{
"model": ["initial", "add_features", "hpo"],
"score": [-114.766567, -35.146287, -41.271647]
}
).plot(x="model", y="score", figsize=(10, 8)).get_figure()
fig.savefig('model_train_score.png')
In [64]:
# Take the 3 kaggle scores and creating a line plot to show improvement
fig = pd.DataFrame(
{
"test_eval": ["initial", "add_features", "hpo"],
"score": [1.39373, 0.46870, 0.49696]
}
).plot(x="test_eval", y="score", figsize=(10, 8)).get_figure()
fig.savefig('model_test_score.png')
In [65]:
pd.DataFrame({
"model": ["initial", "add_features", "hpo"],
"timelimit": ["time_limit = 600", "time_limit=600", "time_limit=600"],
"presets": ["presets='best_quality'", "presets='best_quality'", "presets='best_quality'"],
"hp-method": ["none", "problem_type = 'regression'", "tabular autogluon"],
"score": ["1.39373", "0.46870", "0.49696"]
})
Out[65]:
| model | timelimit | presets | hp-method | score | |
|---|---|---|---|---|---|
| 0 | initial | time_limit = 600 | presets='best_quality' | none | 1.39373 |
| 1 | add_features | time_limit=600 | presets='best_quality' | problem_type = 'regression' | 0.46870 |
| 2 | hpo | time_limit=600 | presets='best_quality' | tabular autogluon | 0.49696 |
In [66]:
def plot_series(time, series, format="-", start=0, end=None, label=None):
plt.plot(time[start:end], series[start:end], format, label=label)
plt.xlabel("Time")
plt.ylabel("Value")
if label:
plt.legend(fontsize=14)
plt.grid(True)
In [67]:
sub_new = pd.read_csv('submission_new_features.csv')
In [ ]:
import matplotlib.pyplot as plt
series = train["count"].to_numpy()
time = train["datetime"].to_numpy()
plt.figure(figsize=(350, 50))
plot_series(time, series)
plt.title("Train Data time series graph")
#plot_series(time1, series1)
plt.show()
